mice (version 3.3.0)

mice.impute.norm.boot: Imputation by linear regression, bootstrap method

Description

Imputes univariate missing data using linear regression with bootstrap

Usage

mice.impute.norm.boot(y, ry, x, wy = NULL, ...)

Arguments

y

Vector to be imputed

ry

Logical vector of length length(y) indicating the the subset y[ry] of elements in y to which the imputation model is fitted. The ry generally distinguishes the observed (TRUE) and missing values (FALSE) in y.

x

Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.

wy

Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.

...

Other named arguments.

Value

Vector with imputed data, same type as y, and of length sum(wy)

Details

Draws a bootstrap sample from x[ry,] and y[ry], calculates regression weights and imputes with normal residuals.

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. https://www.jstatsoft.org/v45/i03/

See Also

Other univariate imputation functions: mice.impute.cart, mice.impute.lda, mice.impute.logreg.boot, mice.impute.logreg, mice.impute.mean, mice.impute.midastouch, mice.impute.norm.nob, mice.impute.norm.predict, mice.impute.norm, mice.impute.pmm, mice.impute.polr, mice.impute.polyreg, mice.impute.quadratic, mice.impute.rf, mice.impute.ri